Interpretable reparameterisations of citation models

Żogała-Siudem, Barbara; Cena, Anna; Siudem, Grzegorz; Gągolewski, Marek

doi:10.1016/j.joi.2022.101355

Cited by 1 publication

(2 citation statements)

References 26 publications

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“…Additionally, some scholars have observed that in citation analysis, scientific paper and patents convey remarkably similar information regarding technology, whether in terms of temporal distribution or the degree of association with specific technologies [45,46]. In recent years, a substantial number of scholars have continued to investigate technology through the concept of knowledge flow in their research [42,[47][48][49]. Park et al [42] proposed a future-oriented approach to discovering technological opportunities for convergence using patent information and a link prediction method in a directed network.…”

Section: Technology Assessmentmentioning

confidence: 99%

“…Chen et al [47] tested the hypothesis that patent citations indicate knowledge linkage by measuring text similarities between citing-cited patent pairs and finds that examiner citations are a better indicator of knowledge linkage than applicant citations. Żogała-Siudem et al [48] introduced a reparametrized version of the discrete generalized beta distribution and power law models that preserve the total sum of elements in a citation vector, resulting in better predictive power and easier numerical fitting. It is worth noting that, when employing citation analysis to explore mutual citations among technologies, both patents and scientific paper hold equal importance.…”

Section: Technology Assessmentmentioning

confidence: 99%

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A Cross-Citation-Based Model for Technological Advancement Assessment: Methodology and Application

Tang,

Cai,

Xiao

2024

Sustainability

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Assessing technological advancements is crucial for the formulation of science and technology policies and making well-informed investments in the ever-evolving technology market. However, current assessment methods are predominantly geared towards mature technologies, limiting our capacity for a systematic and quantitative evaluation of emerging technologies. Overcoming this challenge is crucial for accurate technology evaluation across various fields and generations. To address this challenge, we present a novel approach that leverages bibliometrics, specifically paper citation networks, to gauge shifts in the flow of knowledge throughout the technological evolution. This method is capable of discerning a wide array of trends in technology development and serves as a highly effective tool for evaluating technological progress. In this paper, we showcase the accuracy and applicability of this approach by applying it to the realm of mobile communication technology. Furthermore, we provide a comparative analysis of its quantitative results with other conventional assessment methods. The practical significance of our model lies in providing a nuanced understanding of emerging technologies within a specific domain, enabling informed decisions, and fostering strategic planning in technology-oriented fields. In terms of originality and value, this model serves as a comprehensive tool for assessing technological progress, quantifying emerging technologies, facilitating the evaluation of diverse technological trajectories, and efficiently informing technology policy-making processes.

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